Credit-risk evaluation is a challenging and important task in the domain of financial analysis for which many classification methods have been suggested. In this paper, we present the results for eight real-life credit-risk two-class mixed datasets (i.e., discrete and continuous attributes) analyzed by the Three-MLP Ensemble Re-RX algorithm (shortened to “Three-MLP Ensemble”). Clarifying the neural network decisions by explanatory rules that capture the learned knowledge embedded in the networks can help a credit-risk manager explain why a particular applicant is classified as either bad or good. To compare the Three-MLP Ensemble performance, we executed comprehensive rule extraction experiments on eight two-class mixed datasets commonly used for benchmarking studies in credit-risk evaluation. The extremely high accuracy of the Three-MLP Ensemble outperformed the accuracies by the Re-RX algorithm and a variant. In this study, we also compared the accuracy of the Three-MLP Ensemble with that of classifiers recently proposed. It is concluded that neural network rule extraction by the Three-MLP Ensemble is a powerful management tool that allows us to build advanced, comprehensible, and accurate decision-support systems for credit-risk evaluation.